Dynamic image correction and imaging systems

ABSTRACT

A method, system and software are disclosed for applying an image mask for improving image detail in a digital image. An electronic representation of an image is scanned or captured using an image capture device. A dynamic image mask is generated from the electronic representation of the image. The dynamic image mask has sharp edges which are representative of rapidly changing boundaries in the original image and blurred regions in less rapidly changing areas. The dynamic image mask is applied to the electronic representation of the original image to produce an enhanced image. The enhanced image may have certain advantages. For example, in some embodiments, the enhanced image can be view on a display with much more viewing detail that conventional systems.

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of the following U.S.Provisional Patent Applications: Serial No. 60/234,520, filed on Sep.21, 2000, and entitled “Method of Generating an Image Mask for ImprovingImage Detail;” Serial No. 60/234,408, filed on Sep. 21, 2000, andentitled “Method of Applying An Image Mask For Improving Image Detail;”and Serial No. 60/285,591, filed on Apr. 19, 2001, and entitled “Methodand System and Software for Applying an Image Mask for Improving ImageDetail;” of common assignee herewith.

FIELD OF THE INVENTION

[0002] The present invention relates generally to imaging systems andimage processing and more particularly to dynamic image correction andimaging systems.

BACKGROUND OF THE INVENTION

[0003] A variety of methods are commonly employed to capture an image.For example, photographic film may be exposed to light reflected from adesired subject to record a latent image withing the film. The film isthen developed to generate a “negative” or “positive” from which printsor transparencies can be made and delivered to consumers. The negative,positive, or print can be scanned to produce a digital representation ofthe subject. Alternately, digital devices such as digital camera, videorecorder, and the like, may be used to directly capture a digitalrepresentation of the desired subject by measuring the reflected lightfrom the subject.

[0004] Lighting is particularly important when capturing images and careis often take to ensure the proper lighting of the subject matter of theimage. If too much light is reflected from the subject, the capturedimage will be over-exposed, and the final image will appear washed-out.If too little light, the captured image will appear under-exposed, andthe final image will appear dark. Similarly, if the proper lighting isnot provided from a proper angle, for example when one part of an imageis in bright light while another part is in shadow, some of the imagemight be properly exposed, while the remainder of the image is eitherunder-exposed or over-exposed. Conventional digital devices areparticularly prone to having over-exposed and under-exposed portions ofan image.

[0005] If during an image capture process the subject is over-exposed orunder-exposed, the mistake can sometimes be minimized in the processing(or development) and/or printing process. Typically, when an image iscaptured on film, the negative contains much more image detail than canbe reproduced in a photographic print, and so a photographic printincludes only a portion of the information available to be printed.Similarly, images captured directly by digital devices often haveconsiderably more image detail then can be reproduced or output. Bychoosing the proper portion of the image detail to print, the finalprocessed image may be compensated for the mistakes made during imagecapture. However, particularly in the case in which some areas of animage are underexposed and other areas of an image are over-exposed, itis difficult to correct both the under-exposed and over-exposed portionsof the image.

[0006] Conventional correction techniques for reducing the effects ofover-exposed and under-exposed regions are generally performed by handand can be extremely expensive. One conventional correction technique isto apply a cutout filter. In this technique, the image is divided intolarge, homogeneous regions, and a filter is applied to each of theseregions. Referring now to FIG. 1, in which a conventional cutout filter110 is shown. The original image is of a castle. Assume that in theoriginal image, the sky 160 lacks detail and is washed out, while thecastle 120 is in shadow. The cutout filter 110 has a dark sky 160 and alight castle 120, so that when applied to the original image, the sky160 in the resultant image will be darker, and the castle 120 will belighter, thereby improving “gross” image detail.

[0007] A drawback of cutout filter 110 is that image detail within theregions is not properly corrected unless the selected region is trulyhomogeneous, which is not very likely. As a result, detail within eachregion is lost. The number of regions selected for filtering may beincreased, but selecting more regions greatly increases the time andlabor needed to generate the cutout filter 110. In addition, thistechnique and other conventional techniques tends to create visuallyunappealing boundaries between the regions.

SUMMARY OF THE INVENTION

[0008] In accordance with one implementation of the present invention amethod of enhancing an image is provided. In one embodiment, the methodcomprises obtaining an image mask of the original image. The image maskand the original image each comprise a plurality of pixels havingvarying values. The plurality of mask pixels are set to form sharperedges corresponding to areas of more rapidly changing pixel values inthe original image. The pixels are further arranged to form areas ofless sharp regions corresponding to areas of less rapidly changing pixelvalues in the original image. The method further comprises combining theimage mask with the original image to obtain a masked image.

[0009] Another embodiment of the present invention provides for adigital file tangibly embodied in a computer readable medium. Thedigital file is generated by implementing a method comprising obtainingan image mask of an original image. The image mask and the originalimage each comprise a plurality of pixels having varying values. Theplurality of mask pixels are set to form sharper edges corresponding toareas of more rapidly changing pixel values in the original image. Thepixels are further arranged to form areas of less sharp regionscorresponding to areas of less rapidly changing pixel values in theoriginal image. The method further comprises combining the image maskwith the original image to obtain a masked image.

[0010] An additional embodiment of the present invention provides for acomputer readable medium tangibly embodying a program of instructions.The program of instructions is capable of obtaining an image mask of anoriginal image. The image mask and the original image each comprise aplurality of pixels having varying values. The plurality of mask pixelsare set to form sharper edges corresponding to areas of more rapidlychanging pixel values in the original image. The pixels are furtherarranged to form areas of less sharp regions corresponding to areas ofless rapidly changing pixel values in the original image. The program ofinstructions is further capable of combining the image mask with theoriginal image to obtain a masked image.

[0011] Yet another embodiment of the present invention provides for asystem comprising an image sensor to convert light reflected from animage into information representative of the image, a processor, memoryoperably coupled to the processor, and a program of instructions capableof being store in the memory and executed by the processor. The programof instructions manipulate the processor to obtain an image mask, theimage mask and the information representative of the image eachincluding a plurality of pixels having varying values, wherein thevalues of the plurality of mask pixels are set to form sharper edgescorresponding to areas of more rapidly changing pixel values in theoriginal image and less sharp regions corresponding to areas of lessrapidly changing pixel values in the original image. The program ofinstructions also manipulate the processor to combine the image maskwith the information representative of the image to obtain a maskedimage.

[0012] An advantage of at least one embodiment of the present inventionis that an image to improve reproducible detail can be generated withoutuser intervention.

[0013] An additional advantage of at least one embodiment of the presentinvention is that an image mask can be automatically applied to anoriginal image to generate an image with improved image detail within areproducible dynamic range due to the image detail preserved in theimage mask.

[0014] Yet another advantage of at least one embodiment of the presentinvention is that calculations to improve the image detail in scannedimages can be performed relatively quickly, due to a lower processingoverhead and less user intervention than conventional methods.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Other objects, advantages, features and characteristics of thepresent invention, as well as methods, operation and functions ofrelated elements of structure, and the combination of parts andeconomies of manufacture, will become apparent upon consideration of thefollowing description and claims with reference to the accompanyingdrawings, all of which form a part of this specification, wherein likereference numerals designate corresponding parts in the various figures,and wherein:

[0016]FIG. 1 is an illustration showing a conventional cutout filter;

[0017]FIG. 2 is a block diagram illustrating a method for dynamic imagecorrection according to one embodiment of the present invention;

[0018]FIG. 3 is a block diagram of an original image and a dynamic imagemask according to one embodiment of the present invention;

[0019]FIG. 4 is a set of graphs showing intensity values of pixelsaround an edge before and after a blurring algorithm has been appliedaccording to one embodiment of the present invention;

[0020]FIG. 5 is a block diagram of a method for generating a dynamicimage mask according to at least one embodiment of the presentinvention;

[0021]FIG. 6 is a representation of an dynamic image mask withproperties according to at least one embodiment of the presentinvention;

[0022]FIG. 7 is a block diagram illustrating a method of applying adynamic image mask to an image according to at least one embodiment ofthe present invention;

[0023]FIG. 8A is a block diagram illustrating a wrinkle reductionprocess in accordance with one embodiment of the invention;

[0024]FIG. 8B-1 is a picture illustrating an original image;

[0025]FIG. 8B-2 is a picture illustrating the image of 8B-1 with thewrinkle reduction process applied;

[0026]FIG. 9 is a block diagram illustrating an image capture systemaccording to at least one embodiment of the present invention; and

[0027]FIG. 10 is a chart illustrating improvements in the dynamic rangeof various image representations according to at least one embodiment ofthe present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0028] FIGS. 2-9 illustrate a method for dynamic image correction andimaging systems having enhanced images. As described in greater detailbelow, one embodiment of dynamic image correction utilizes a dynamicimage mask that uses a blurring algorithm that maintains sharpboundaries of the image. The dynamic image mask is then applied to theimage. In some implementations, the dynamic image mask is used toincrease the amount of reproducible detail within an image. In anotherimplementation, the dynamic image mask is used to suppress medianfrequencies and maintain sharp boundaries. In this implementation, thedynamic image mask can be regionally applied using an electronic brush.In yet other implementations, various embodiments of the dynamic imagemask can be used as a correction map for other correction andenhancement functions. Systems for utilizing digital image correctioncan include a variety of image capturing or processing systems, such asdigital cameras, video cameras, scanners, image processing software, andthe like.

[0029] Referring now to FIG. 2, one method of dynamic image correction200 is described. In this embodiment, dynamic image correction 200includes creating a dynamic image mask B from an original image A. Thedynamic image mask B be is then combined with original image A togenerate an enhanced image C. In one embodiment, the enhanced image Chas improved image detail over original image A, within a reproducibledynamic range. For example, original image A may contain detail whichmay not be appropriately represented when output for display orprinting, such as containing high contrast over-exposed (bright) regionsand under-exposed (shadow) regions. It would be helpful to brighten thedetail in the shadow regions and decreasing the brightness of the brightregions without losing image detail. At least one embodiment of thepresent invention automatically performs this function. In contrast,conventional methods of simply dividing the original image into a brightand shadow regions will not generally suffice to improve complex images.Images generally contain complex and diverse regions of varying contrastlevels, and as a result, conventional methods generally produceinadequate results.

[0030] In step 210, original image A is provided. Original image A is anelectronic representation of a subject and includes one or morecharacteristic values corresponding to specific locations, or pixels.Each pixel has one or more associated values, or planes, that representsinformation about a particular location on the subject. For originalimage A, the values corresponding to each pixel can be a measure of anysuitable characteristic of the subject. For example, the values mayrepresent the color, colors, luminance, incidence angle, x-ray density,or any other value representing a characteristic or combination ofcharacteristics.

[0031] Original image A can be obtained in any suitable manner and neednot correlate directly to conventional color images. One implementationobtains original image A by digitizing an image using a scanner, such asa flatbed, film scanner, and the like. Another implementation obtainsoriginal image A by directly capturing the image using a digital device,such as a digital camera, video camera, and the like. In yet anotherimplementation, the original image A is captured using an imagingdevice, such as magnetic resonance imaging system, radar system, and thelike. In this embodiment, the characteristic values do not correlate tocolors but to other characteristics of the subject matter imaged. Theoriginal image A could also be obtained by computer generation or othersimilar technique. Dynamic image correction 200 does not depend upon howthe original image A is obtained, but only that the original image Aincludes one or more values that represent the image.

[0032] In step 220, a dynamic image mask B is generated from originalimage A. In the preferred embodiment, the pixel values of the dynamicimage mask B are generated relative to a pixel in the original image A.In at least one embodiment, the pixels generated for dynamic image maskB are calculated using weighted averages of select pixels in originalimage A, as discussed in greater detail below. It will be appreciatedthat the pixels generated for dynamic image mask B may be calculatedusing any number of methods without departing from the spirit or thescope of the present invention.

[0033] Dynamic image mask B maintains the sharp edges in the originalimage A while blurring regions the surrounding the sharp edges. Ineffect, rapidly changing characteristics, i.e., values or contrast, inoriginal image A are used to determine sharp edges in dynamic image maskB. At the same time, less rapidly changing values in original image Acan be averaged to generate blurred regions in dynamic image mask B. Ineffect, the calculations performed on original image A produce a dynamicimage mask B which preserves the boundaries between dissimilar pixels inoriginal image A while blurring areas containing similar pixels, as willbe discussed further in FIG. 3.

[0034] A dynamic image mask B is often calculated for eachcharacteristic value. For example, in the case of an original imagehaving red, green, and blue values for each pixel, the red values areused to calculate the blurring and edge parameters of the dynamic imagemask B for the red color, the blue values are used to calculate theblurring and edge parameters of the dynamic image mask B for the bluecolor, and so on. The dynamic image mask B can use differentcharacteristics, or planes, to establish the regions and boundaries fordifferent characteristics. For example, in the case of an original imagehaving red, green, and blue color values for each pixel, the red valuescould be used to establish the blurring and edge parameters that areapplied to each of the red, green, and blue values. Similarly, acalculated luminance value could be used to calculate the blurring andedge parameters that are then applied to the red, green, and blue valuesof each pixel. In other embodiments, a dynamic image mask B is onlycalculated for certain characteristics. Using the same example as above,a dynamic image mask B for the colors red and green may be calculated,but the values of the color blue are combined without change, asdescribed in greater detail below.

[0035] In step 230, dynamic image mask B is applied to original image Ato produce enhanced image C. Dynamic image mask B is generally appliedto original image A by use of an overlay technique. As discussed furtherin FIG. 7, a mathematical operation, such as division between the pixelvalues of original image A and the corresponding pixel values in dynamicimage mask B, can be used to generate the pixel values of enhanced imageC.

[0036] In general, the process of generating and applying the dynamicimage mask B is performed as part of a set of instructions run by aninformation processing system. The processes of steps 210, 220, and 230can be performed within an image processing system, implemented byphoto-lab technicians, in a system used by a customer without theassistance of a lab technician, incorporated into a scanner, digitalcamera, video recorder and the like, or performed by a computer systemexternal to the image capturing device. In at least one embodiment, theprocesses are automated by a program of executable instructions executedby an information processing system such that minimal user interactionis required.

[0037] In step 240, the enhanced image C is delivered in the formdesired. The form in which the enhanced image C is delivered includes,but is not limited to, a digital file, a photographic print, or a filmrecord. Digital files can be stored on mass storage devices, tapedrives, CD recorders, DVD recorders, and/or various forms of volatile ornon-volatile memory. Digital files can also be transferred to othersystems using a communications adapter, where the file can be sent tothe Internet, an intranet, as an e-mail, etc. A digital file can also beprepared for retrieval at an image processing kiosk which allowscustomers to recover their pictures and print them out in a form oftheir choosing without the assistance of a film development technician.The enhanced image C can also be displayed as an image on a display orprinted using a computer printer. The enhanced image C also can berepresented on a form of film record, such as a film negative, positiveimage, or photographic print. In conventional printing processes, whenan image is printed, a large portion of the dynamic range is lost. Incontrast, enhanced image C generally contains desirable detail fromoriginal image A so that a larger quantity of image detail from originalimage A is effectively compressed into a dynamic range capable of beingreproduced in print and can be preserved thereby.

[0038] Referring now to FIG. 3, a diagram of an original image and ablurred image are shown, according to one embodiment of the presentinvention. Original image A is composed of a plurality of pixels, suchas pixels numbered 301-325. Dynamic image mask B is composed ofcorresponding pixels, such as pixels numbered 351-375, calculated fromthe pixels of original image A. As described in greater detail below,the pixel values of dynamic image mask B are calculated using anaveraging function that accounts for sharp edges.

[0039] A sharp edge is generally defined by a variation between pixelvalues greater than a certain sharpness threshold, or Gain. In effect,the sharpness threshold allows the pixels to be differentiated intoregions for purposes of averaging calculations. In some embodiments, thesharpness threshold is varied by a user. In other embodiments, thesharpness threshold is fixed within the software.

[0040] The pixels calculated for dynamic image mask B correspond toaverages taken over regions of pixels in original image A, taking intoaccount the sharpness threshold, or Gain. For example, pixel 363corresponds to calculations performed around pixel 313. In oneembodiment, provided that pixels 301-325 are similar, i.e., differenceis below the sharpness threshold, pixel 363 is calculated by averagingthe values of pixels 311-315, 303, 308, 318, and 323. In anotherembodiment, pixel 363 is calculated by averaging the values of pixels307-309, 312-314, and 317-319. Any suitable number or selection processfor the averaging process may be used without departing from the scopeof the present invention. In the preferred embodiment, the pixels areassigned a weight based on their relative distance from pixel 313. Inthis embodiment, pixels that are relatively closer have a greater impacton the averaging calculation that pixels that are relatively remote.

[0041] In one embodiment of the present invention, the dynamic imagemask B is calculated using a weighting function as described by thefollowing equation:$w_{N} = ( {1 - {( {\frac{{{pixel}\quad N} - {centerpixel}}{Gain}} ).}} $

[0042] The weight function, w_(N), can be used to apply a separateweight to each of the pixel values. Only values of w_(N) between zeroand one are accepted. Accordingly, if the value of w_(N) is returned asa negative value, the returned weight for the pixel being weighed iszero. Using the first example above, if pixel 313 was being calculated,w_(N) could be used to apply a weight to each of the pixels 311-315,303, 308, 318, and 323. PixelN is the contrast value of the pixel beingweighed. Center pixel is the value of the central pixel, around whichthe blurring is being performed. Gain is a threshold value used todetermine a contrast threshold for a sharp edge. For example, if pixel362 is being calculated and the difference in contrast between pixel 313and pixel 308 is 15, with Gain set to 10, the returned value of w_(N) isnegative. Accordingly, since negative values are not allowed, pixel 308is assigned a weight of zero, keeping the value of pixel 308 fromaffecting the calculation of pixel 362.

[0043] The value of Gain can be decreased as the pixel being weighed isfurther from the central pixel. Lowering the value of Gain allows smallchanges in the contrast between pixelN and centerpixel to result innegative w_(N), and thus be weighed to zero. Accordingly, in oneembodiment, the farther the pixel is from the centerpixel, the smallerGain gets and the more likely it is that the value of w_(N) will benegative and the pixel will be assigned a weight of zero. The choice ofGain is chosen to preferably decrease slowly as the distance from thecentral pixel is increased. The values of Gain used can be adapted forthe desired application; however, it has been found that slower changesin Gain provide images with more pleasing detail than sharper changes inGain. Furthermore, the weight function itself can be altered withoutdeparting from the scope of the present invention.

[0044] Once the weights of the surrounding pixels have been calculated,a sum of each of the pixel values, multiplied by their relative weights,can be calculated. The sum can then be divided by the sum of theindividual weights to generate the weighted average of the pixels, whichcan be used for the pixels of dynamic image mask B. The minimum weightcalculated from the pixels adjacent to the central pixel can also beused and multiplied by each of the pixels surrounding the central pixel.Multiplying by the weight of an adjacent pixel allows the blurring to beeffectively turned “off” if the contrast around the central pixel ischanging too rapidly. For example, if the difference in contrast betweena central pixel and an adjacent pixel is large enough to warrant a sharpedge in dynamic image mask B, the weight of the adjacent pixel will bezero, forcing all other values to zero and allowing the central pixel toretain its value, effectively creating a sharp edge in dynamic imagemask B.

[0045] This embodiment of the processes performed to generate dynamicimage mask B can be likened to a sandblaster. A sandblaster can be usedto soften, or blur, the textures it is working over. Accordingly, theblurring algorithm as described above will be herein referred to as thesandblaster algorithm. A sandblaster has an effective radius over whichit is used, with the material closer to the center of the sandblastingradius affected most. In the blurring algorithm described, a radius isselected and measured from the central pixel. The pressure of asandblaster can be adjusted to affect more change. The Gain value in thedescribed algorithm can be altered to affect more or less blurring. Inat least one embodiment, the preferred radius is 4 and the preferredGain is 40.

[0046] The sandblaster algorithm can be performed in one dimensionalincrements. For example, to calculate the value of pixel 362, the pixelssurrounding pixel 312 are considered. In one embodiment of the presentinvention, the averaged pixel values are determined using theneighboring vertical pixels and then the neighboring horizontal pixelvalues, as described above. Alternatively, windows can be generated andapplied to average in pixels around the central pixel together, in boththe horizontal and vertical directions. Color images can composemultiple image planes, wherein the multiple image planes may includeplanes for each color, a red plane, a green plane, and a blue plane. Ina preferred embodiment, the sandblaster algorithm is only performed onone plane at a time. Alternatively, the sandblaster algorithm can becalculated taking other image planes into account, calculating in thevalues of pixels relative to the central pixel from different colorplanes. However, it should be noted that performing multi-dimensionalcalculations over an image may increase the processing time.Additionally, pixels which are near an image edge, such as pixel 311 mayignore values desired from pixels beyond the limits of original image A.In one embodiment, the images along the edge use their value toreproduce pixel values beyond the image edge, for calculation with thesandblaster algorithm. Additionally, zeroes may be used for values lyingoutside the edges of original image A.

[0047] Referring now to FIG. 4, a graph of intensities across a row ofintensities, before and after the sandblaster blurring algorithm hasbeen applied is shown, according to at least one embodiment of thepresent invention. Graph 450 represents the intensity values in anoriginal image A around an edge representing contrasting intensity.Graph 460 represents the intensities for dynamic image mask B, among thesame pixels as graph 450.

[0048] Two distinct intensity levels are identifiable in graph 450. Alow intensity can be identified among pixels 451-454 and a highintensity region can be identified by pixel 465. The radius used to blurthe pixels around the central pixel described in FIG. 3 is one factor inhow much blurring will be performed. If too large a radius is used,little blurring may result. For example, if the pixel considered forblurring was pixel 451 and the radius was set large enough, the blurredvalue of pixel 452 may not change much. With the radius set largeenough, pixel 452 will be averaged with many pixels above its intensity,such as pixel 451. Pixel 452 will also be averaged with many pixelsbelow its intensity, such as pixel 453. If the radius is too large,there could be enough pixels with intensities above pixel 452 and enoughpixels with intensities below pixel 452 that the value for pixel 452will remain unchanged since the intensity value of pixel 452 liesbetween the high and low extremes.

[0049] Little blurring could also result from selecting too small aradius for blurring. In selecting a small radius, only the intensityvalues of pixels immediately by the selected pixel will be considered.For example, selecting pixel 452 as the central pixel. If the radius istoo small, allowing pixels only as far as pixel 451, pixels 453 and 454may not be considered in the blurring around pixel 452. Selection of theradius has drastic effects to how much blurring is accomplished. Theblurring radius must be large enough to average enough of a region ofpixels while being small enough to effect enough blurring. In oneembodiment, the blurring radius can be controlled automatically. Asshown in FIG. 5, blurring can be performed over decimatedrepresentations of an original image using pyramidal decomposition. Byperforming a blurring algorithm and decimating the image, the effectiveradius of the blur is automatically increased as the image resolution isdecreased. A decimated representation of the original image A cancontain half the resolution of the original image A. Some of the detailin the original image is lost in the decimated representation.Performing blurring on the decimated image with a specific radius canrelate to covering twice the radius in the original image.

[0050] Graph 462 shows a graph of intensities in the dynamic image maskA using the sandblaster blurring algorithm. As can be seen, the blurringis enough to bring down the intensity of pixel 452 in the original imageto pixel 462 in the blurred representation. Pixel 455, in a separateintensity level is increased in intensity to pixel 465 in the blurredrepresentation. In at least one embodiment, the blurring is turned offfor pixels along an edge. Turning off the blurring allows the sharpnessamong edges to be preserved in the blurred representation, preservingedges between regions with a high contrast of intensities. Pixel 454lies along an edge, where the intensity for pixels nearby, such as pixel455, is much higher. The intensity of pixel 454 is not changed,preserving the difference in contrast between pixel 454 and the pixelsof higher intensity, such as pixel 455.

[0051] Referring now to FIG. 5, a block diagram of a method forgenerating another embodiment of a dynamic image mask B is illustrated.In this embodiment, the sandblaster algorithm can be used to create ablurred image with sharp edges and blurred regions. To improve thedetail captured by an image mask incorporating the sandblaster blurringalgorithm, a pyramidal decomposition is performed on the original image,as shown in FIG. 5. In step 510, the original image A is received.

[0052] In step 535, the image size is reduced. In at least oneembodiment, the image size is reduced in half. The reduction in imagesize may be performed using a standard digital image decimation. In oneembodiment, the decimation is performed by discarding every other pixelin the original image from step 510.

[0053] In step 525, the sandblaster algorithm, as discussed in FIG. 3,is performed on the decimated image to create a blurred image. Aspreviously discussed for FIG. 4, the decimated image contains half theresolution of the original image A. Some of the detail in the originalimage A is lost to the decimated image. By performing the sandblasteralgorithm on the decimated image, the effective radius covered by thealgorithm can relate to twice the radius in the original image. Sincesome of the detail from the original image A is not present in thedecimated image, more blurring can result with the sandblasteralgorithm. As the images described herein are decimated, the effectiveblur radius and amount of detail blurred increased in inverse proportionto the change in resolution in the decimated images. For example,performing the sandblast algorithm in step 525 to the reduced image ofstep 535 has twice the effective radius of performing the same algorithmto the original image, while the reduced image has half the resolutionof the original image.

[0054] In step 536, the blurred image is decimated once again. In step526, the decimated image from step 536 is blurred using the sandblasteralgorithm. Further decimation steps 537-539 and sandblaster steps527-329 are consecutively performed on the outputs of previous steps. Instep 550, the blurred image from the sandblaster step 529 is subtractedfrom the decimated output of decimation step 550. In step 560, the mixedoutput from step 550 is up-sampled. In one embodiment, the image isincreased to twice its pixel resolution. Increasing the image size maybe performed by repeating the image values of present pixels to fill newpixels. Interpolation may also be performed to determine the values ofthe new pixels. in step 552, the up-sampled image from step 560, isadded to the blurred image from step 528. The combined image informationis subtracted from the decimated output from step 538. The calculationsin step 552 are performed to recover image detail that may have beenlost. Mixer steps 554 and 552, consecutively performed with up-samplingsteps 562-366, attempt to generate mask data. In step 558, a mixer isused to combine the up-sampled image data from step 566 with the blurredimage data from step 525. The output from the mixer in step 558 is thenup-sampled, in step 580, to produce the image mask of the receivedimage. The dynamic image mask B is then prepared for delivery and use,as in step 590.

[0055] It will be appreciated that additional or less blurring may beperformed among the steps of the pyramidal decomposition describedherein. It should be noted that by not performing the blurring algorithmon the original image, a significant amount of processing time may besaved. Calculations based on the decimated images can be performedfaster and with less overhead than calculations based off the originalimage, producing detailed image masks. The image masks produced usingthe described method preferably include sharp edges based on rapidlychanging boundaries found in the original image A, and blurred regionsamong less rapidly changing boundaries. It should also be appreciatedthat more or less steps may be performed as part of the pyramidaldecomposition described herein, without departing from the scope of thepresent invention.

[0056] In the described embodiment, pyramidal decomposition is performedalong a single image color plane. It will be appreciated that additionalcolor planes may also be presented in the steps shown. Furthermore,multi-dimensional processing, wherein information from different colorplanes or planes of brightness is processed concurrently, may also beperformed. According to at least one embodiment of the presentinvention, the resultant image mask generated is a monochrome mask, usedto apply itself to the intensities of the individual image color planesin the original image. A monochrome image plane can be calculated fromseparate image color planes. For example, in one embodiment, the valuesof the monochrome image mask are determined using the followingequation:

OUT=MAX(R,G).

[0057] OUT refers to the pixel being calculated in the monochromaticimage mask. MAX(R,G) is a function in which the maximum intensitybetween the intensity value of the pixel in the red plane and theintensity value of the pixel in the green plane is chosen. In the caseof a dynamic image mask pixel which contains more than 80% of itsintensity from the blue plane, the formula can be appended to include:

OUT=OUT+50% B.

[0058] wherein 50% B is half of the intensity value in the blue plane.The dynamic image mask B may also be made to represent imageintensities, such as the intensity among black and white values. It willbe appreciated that while full color image masks may be used, they willrequire more processing overhead than using monochrome masks.

[0059] Referring now to FIG. 6, a dynamic image mask B is illustrated,in comparison to a prior-art conventional cutout filter shown in FIG. 1,with properties representative of an dynamic image mask B createdaccording to at least one embodiment of the present invention. Thedynamic image mask B shown in FIG. 6 will be generally referred to asrevelation mask 650. The conventional image mask shown in FIG. 1(prior-art) will be generally referred to as conventional filter 110.

[0060] The revelation mask 650 maintains some of the detail lost toconventional image masks. Edges are preserved between regions of rapidlychanging contrasts. For example, light region 690, generated to brightendetail within windows in the original image, maintains edges to showsharp contrast to the darker region 680, which is generated to darkendetails in the walls shown in the original image. It should be notedthat while edges are maintained between regions of rapidly changingcontrasts, blurring is accomplished within the regions. For example, thedetails in the roof of the original image contain dark and light areaswith a gradual shift in contrast. In conventional filter 110, darkregion 127 is generated to maintain contrast with the lighter areas inthe tower on the roof. When conventional filter 110 is overlaid with theoriginal image, the resultant image will show a sharp contrastdifference between dark region 127 and light region 120 which does notmaintain the gradual difference in the original image. In comparison,revelation mask 650 maintains the gradual shift in contrast as can benoted by the blurred shift in intensity between the tower region 655 andthe lighter region 670, allowing the roof in the original image tomaintain a gradual shift in intensity contrast while maintaining thesharp contrast of dark region 655 against the darker region 660,representing the background sky in the original image.

[0061] Referring now to FIG. 7, a method for generating an enhance imageC in accordance with one embodiment of the present invention isillustrated. Image information related to an original image A ismathematically combined with information from dynamic image mask B. Thecombined data is used to create the enhanced image C.

[0062] The enhanced image C is generated on a pixel by pixel basis. Eachcorresponding pixel from original image A and dynamic image mask B iscombined to form a pixel in masked image 710. For example, pixel datafrom pixel 715, of original image A, is combined with pixel informationfrom pixel 735, of digital image mask B, using mathematicalmanipulation, such as overlay function 720. The combined data is used torepresent pixel 715 of enhanced image C.

[0063] Overlay function 720 is a function used to overlay the pixelinformation between original image A and dynamic image mask B. In oneembodiment of the present invention, overlay function 720 involvesmathematical manipulation and is defined by the equation:${OUT} = {\frac{IN}{{\frac{3}{4}{MASK}} + \frac{1}{4}}.}$

[0064] OUT refers to the value of the pixel in dynamic masked image B.IN refers to the value of the pixel taken from original image A. MASKrefers to the value of the corresponding pixel in enhanced image C. Forexample, to produce the output value of pixel 714, the value of pixel714 is divided by ¾ the value of pixel 734, with the addition of anoffset. The offset, ¼, is chosen to prevent an error from occurring dueto diving by zero. The offset can also be chosen to lighten shadows inthe resultant masked image 710.

[0065] In one embodiment, the application of dynamic image mask B tooriginal image A is performed through software run on a informationprocessing system. As previously discussed, dynamic image mask B can bea monochromatic mask. The dynamic image mask B can be used to controlthe white and black levels in images. Grayscale contrast is the contrastover large areas in an image. Image contrast refers to the contrast ofdetails within an image. Through manipulation of the proportion of thevalue of MASK and the offset used in overlay function 720, the grayscalecontrast and the image contrast can be altered to best enhance theenhanced image C 310. In one embodiment of the present invention,overlay function 720 is altered according to settings made by a user.Independent control of the image contrast and grayscale contrast can beprovided. Control can be used to produce images using low image contrastin highlights and high image contrast in shadows. Additionally,functions can be added to control the generation of the dynamic imagemask B. Control can be offered over the pressure (Gain) and radius(region) effected through the sandblaster algorithm (described in FIG.3). Additionally, control over the histogram of the image can be offeredthrough control over the image contrast and the grayscale contrast. Anormalized image can be generated in which histogram leveling can beperformed without destroying image contrast. The controls, functions,and algorithms described herein can be performed within an informationprocessing system. It will be appreciated that other systems may beemployed, such as through image processing kiosks, to produce enhancedimage C, in keeping with the scope of the present invention.

[0066] Referring to FIG. 8A, a wrinkle reduction process 800 inaccordance with one embodiment of the present invention is illustrated.As described in greater detail below, this embodiment of the wrinklereduction process 800 operates to suppress median frequencies withoutsuppressing high definition detail or low frequency contrast. As aresult, people have a younger look without sacrificing detail.

[0067] In the embodiment illustrated, a dynamic image mask B iscalculated from original image A, as shown by block 802. In thepreferred embodiment, the dynamic image mask B is calculated using aradius of 5 and a Gain of 64, as discussed in FIG. 3. The dynamic imagemask B is then passed through a low pass filter 804. The low pass filter804 is preferably a “soft focus” filter. In one embodiment, the low passfilter 804 is calculated as the average of a Gaussian average with aradius of one and a Gaussian average with a radius of three. Other typesof low pass filters may be used without departing from the scope of thepresent invention.

[0068] The original image A is also passed through a high pass filter806. In one embodiment, the high pass filter 806 is calculated as theinverse of the average of the Gaussian average with a blur of one and agaussian average with a blur of three. Other types of high pass filtersmay be used without departing from the scope of the present invention.

[0069] The results from the low pass filter 804 and the high pass filter806 are then added together to form a median mask 808. The median mask808 can then be applied to the original image A using, for example,applicator 810 to produce an enhanced image. In the preferredembodiment, the applicator 810 is an electronic brush that can be variedby radius to apply the median mask 808 only to those areas of theoriginal image A specified by the user. Other types of applicators 810may be used to apply the median mask 808 to the original image A.

[0070]FIG. 8B-1 illustrates an untouched original image 820, and FIG.8B-2 illustrates the same image after having the wrinkle reductionprocess 800 applied to the image 820. As can be seen, the wrinklereduction process 800 reduces the viable affects of age of the person inthe image, without sacrificing the minute detail of the image andwithout apparent blurring or softening of the details. This creates amore pleasing image to the eye and most importantly, more pleasing tothe person in the picture. The same process can be applied to otherparts of the image to produce similar results. For example, when appliedto clothing, the wrinkle reduction process 800 produces the appearanceof a freshly pressed shirt or pants without affecting the details orappearing blurry. Although only a few of the applications of the wrinklereduction process 800 and dynamic image mask B have been illustrated, itshould be understood that they may be used for any suitable purpose orcombination without departing from the scope of the present invention.

[0071] Referring to FIG. 9, an image capture system 900 used toimplement one or more embodiments of the present invention isillustrated. Image capture system 900 includes any device capable ofcapturing data representative of an image and subsequently processingthe data according to the teachings set forth herein. For example, imagecapture system 900 could include a digital camera, video recorder, ascanner, image processing software, and the like. An embodiment whereimage capture system 900 includes a digital camera is discussedsubsequently for ease of illustration. The following discussion may beapplied to other embodiments of image capture system 900 withoutdeparting from the spirit or scope of the present invention.

[0072] Image capture system 900 includes, but is not limited to, imagesensor 910, analog-to-digital (A/D) convertor 920, color decoder 930,color management system 940, storage system 950, and/or display 960. Inat least one embodiment, image capture system 900 is connected toprinter 980 via a serial cable, printer cable, universal serial bus,networked connection, and the like. Image sensor 910, in one embodiment,captures an image and converts the captured image into electricalinformation representative of the image. Image sensor 910 could includean image sensor on a digital camera, such as a charge coupled device(CCD) sensor, complementary metal oxide semiconductor sensor, and thelike. For example, a CCD sensor converts photons reflected off of ortransmitted through a subject into stored electrical charge at thelocation of each photosite of the CCD sensor. The stored electricalcharge of each photosite is then used to obtain a value associated withthe photosite. Each photosite could have a one-to-one correspondencewith the pixels of the resulting image, or photosites are used inconjunction to determine the value of one or more pixels.

[0073] In one embodiment, image sensor 910 sends electrical informationrepresenting a captured image to A/D convertor 920 in analog form, whichconverts the electrical information from an analog form to a digitalform. Alternatively, in one embodiment, image sensor 910 captures animage and outputs the electrical information representing the image indigital form. It will be appreciated that, in this case, A/D convertor920 would not be necessary.

[0074] It will be appreciated that photosites on image sensors, such asCCDs, often only measure the magnitude or intensity of the lightstriking a photosite. In this case, a number of methods may be used toconvert the intensity values of the photosites (i.e. a black and whiteimage) into corresponding color values for each photosite. For example,one method of obtaining color information is to use a beam splitter tofocus the image onto more than one image sensor. In this case, eachimage sensor has a filter associated with a color. For example, imagesensor 910 could include three CCD sensors, where one CCD sensor isfiltered for red light, another CCD sensor is filtered for green light,and the third sensor is filtered for blue light. Another method is touse a rotating device having separate color filters between the lightsource (the image) and image sensor 910. As each color filter rotates infront of image sensor 910, a separate image corresponding to the colorfilter is captured. For example, a rotating disk could have a filter foreach of the primary colors red, blue and green. In this case, the diskwould rotate a red filter, a blue filter, and a green filtersequentially in front of image sensor 910, and as each filter wasrotated in front, a separate image would be captured.

[0075] Alternatively, a permanent filter could be placed over eachindividual photosite. By breaking up image sensor 910 into a variety ofdifferent photosites associated with different colors, the actual colorassociated with a specific point or pixel of a captured element may beinterpolated. For example, a common pattern used is the Bayer filterpattern, where rows of red and green sensitive photosites are alternatedwith rows of blue and green photosites. In the Bayer filter pattern,there is often many more green color sensitive photosites than there areblue or red color sensitive photosites, as the human eye is moresensitive to green than the others, so more green color informationshould be present for a captured image to be perceived as “true color”by the human eye.

[0076] Accordingly, in one embodiment, color decoder 930 receives thedigital output representing an image from A/D convertor 920 and convertsthe information from intensity values (black-and-white) to color values.For example, image sensor 910 could utilize a Bayer filter pattern asdiscussed previously. In this case, the black-and-white digital outputfrom A/D convertor 920 could be interpolated or processed to generatedata representative of one or more color images. For example, colordecoder 930 could generate data representative of one or more full colorimages, one or more monochrome images, and the like.

[0077] Using the data representative of an image generated by colordecoder 930, in one embodiment, color management system 940 processesthe data for output and/or storage. For example, color management system940 could attenuate the dynamic range of the data from color decoder930. This may be done to reduce the amount of data associated with acaptured image. Color management 940 could also format the data into avariety of formats, such as a Joint Picture Experts Group (JPEG) format,a tagged image file format (TIFF), a bitmap format, and the like. Colormanagement system 940 may perform a number of other processes or methodsto prepare the data representative of an image for display or output,such as compressing the data, converting the data from an analog to adigital format, etc.

[0078] After color management system 940 processes data representativeof an image, the data, in one embodiment, is stored on storage 950and/or displayed on display 960. Storage 950 could include memory, suchas removable flash memory for a digital camera, a storage disk, such asa hard drive or a floppy disk, and the like. Display 960 could include aliquid crystal display (LCD), a cathode ray tube (CRT) display, andother devices used to display or preview captured images. In analternative embodiment, the data representative of an image could beprocessed by printer driver 970 to be printed by printer 970. Printer970 could include a photograph printer, a desktop printer, a copiermachine, a fax machine, a laser printer, and the like. Printer driver970 could be collocated, physically or logically, with printer 970, on acomputer connected to printer 960, and the like. It will be appreciatedthat one or more of the elements of image capture system 900 may beimplemented as a state machine, as combinational logic, as softwareexecutable on a data processor, and the like. It will also beappreciated that the method or processes performed by one or more of theelements of image capture system 900 may be performed by a single deviceor system. For example, color decoder 930 and color management 940 couldbe implemented as a monolithic microprocessor or as a combined set ofexecutable instructions.

[0079] Image capture system 900 can be used to implement one or moremethods of various embodiments of the present invention. The methods,herein referred to collectively as the image mask method, may beimplemented at one or more stages of the image capturing process ofimage system 900. In one embodiment, the image mask method may beapplied at stage 925 between the output of digital data from A/Dconvertor 920 and the input of color decoder 930. In many cases, stage925 may be the optimal location for application of the image maskmethod. For example, if data representative of an image output fromimage sensor 910 is monochrome (or black-and-white) information yet tobe decoded into color information, less information may need to beprocessed using the image mask method than after conversion of the datato color information. For example, if the data were to be decoded intothe three primary colors (red, blue, green), three times of informationmay need to be processed, as there are three colors associated with eachpixel of a captured image. The image mask method, according to at leastone embodiment discussed previously, does not affect the accuracy oroperation of color decoder 930.

[0080] Alternatively, the image mask method may be applied at stage 935between color decoder 930 and color management system 940. In somesituations, the location of stage 935 may not be as optimal as stage925, since there may be more data to process between color decoder 930and color management system 940. For example, color decoder 93 0 couldgenerate data for each of the primary colors, resulting in three timesthe information to be processed by the image mask method at stage 935.An image mask method may also be implemented at stage 945 between colormanagement system 940 and storage 950 and/or display 960. However, sincethe data output by color management system 940 often has been processedwhich may result in compression and/or loss of information and dynamicrange, therefore application of the image mask method at stage 945 maynot generate results as favorable as at stages 925, 935.

[0081] If the captured image is to be printed, the image mask method maybe implemented at stages 965, 975. At stage 965, the image mask methodmay be implemented by printer driver 970, while at stage 965, the imagemask method may be implemented between printer driver 970 and printer980. For example, the connection between a system connected to printerdriver 970, such as a computer, and printer 980 could include softwareand/or hardware to implement the image mask method. However, asdiscussed with reference to stage 945, the data representative of acaptured image to be printed may have reduced dynamic range and/or lossof other information as a result of processing by color managementsystem 940.

[0082] In at least one embodiment, the image mask method performed atstages 925, 935, 945, 965, and/or 975 is implemented as a set ofinstructions executed by processor 942. Processor 942 can include amicroprocessor, a state machine, combinational logic circuitry, and thelike. In one implementation, the set of instructions are stored andretrieved from memory 943, where memory 943 can include random accessmemory, read only memory, flash memory, a storage device, and the like.Note that processor 942, in one embodiment, also executes instructionsfor performing the operations of one or more of the elements of imagecapture system 900. For example, processor 942 could executeinstructions to perform the color decoding operations performed by colordecoder 930 and then execute the set of instructions representative ofthe image mask method at stage 935.

[0083] It will be appreciated that the cost or effort to implement theimage mask method at an optimal or desired stage (stages 925-975) may beprohibitive, resulting in the implementation of the image mask method atan alternate stage. For example, although stage 925 is often the optimallocation for implementation of the image mask method, for reasonsdiscussed previously, it may be difficult to implement the image maskmethod at this location. For example, image sensor 910, A/D convertor920, and color decoder 930 could be implemented as a monolithicelectronic circuit. In this case it might prove difficult to modify thecircuit to implement the method. Alternatively, more than one element ofimage capture system 900, such as color decoder 930 and color managementsystem 940, may be implemented as a single software application. In thiscase, the software application may be proprietary software wheremodification is prohibited, or the source code of the software may notbe available, making modification of the software application difficult.

[0084] In the event that the image mask method may not be implemented inthe optimal location, application of the image mask method in a moresuitable location often will result in improved image quality anddetail. For example, even though the dynamic range of datarepresentative of an image may be reduced after processing by colormanagement system 940, application of the image mask method at stage945, in one embodiment, results in data representative of an imagehaving improved quality and/or detail over the data output by colormanagement system 940. The improved image data may result in an improvedimage for display on display 960, subsequent display when retrieved fromstorage 960, or physical replication by printer 980. It will beappreciated that the image mask method may be employed more than once.For example, the image mask method may be employed at stage 925 toperform an initial compression of the dynamic range of the image, andthen again at stage 945 for to further compress the images dynamicrange.

[0085] Referring now to FIG. 10, a chart showing various improvements inimage types is illustrated according to at least one embodiment of thepresent invention. As discussed previously, an implementation of atleast one embodiment of the present invention may be used to improve thedynamic range of representations of captured images. The horizontal axisof chart 1000 represents the dynamic range of various types of imagerepresentations. The dynamic range of images, as presented to the humaneye, (i.e. “real life”) is represented by range 1006. The dynamic rangedecreases sequentially from real life (range 1006) to printedtransparencies (range 1005), CRT displays (range 1004), glossyphotographic prints (range 1003), matte photographic prints (range1002), and LCD displays (range 1001). Note that the sequence of dynamicranges of various image representations is a general comparison and thesequence of dynamic ranges should not be taken as absolute in all cases.For example, there could exist a CRT display (range 1004) which couldhave a dynamic range greater than printed transparencies (range 1005).

[0086] According to at least one embodiment, by applying an image maskmethod disclosed herein, the dynamic range of the representation of animage may be improved. For example, by applying an image mask methodsometime before data representing an image is displayed on an LCDmonitor, image information having a dynamic range comparable to a glossyphotographic print (range 1003), could be compressed for display on anLCD monitor having a dynamic range 1001, resulting in an improveddisplay image. Likewise, image information having a dynamic rangeequivalent to a CRT display (range 1004) may be compressed into adynamic range usable for matte photographic prints (range 1002), and soon. As a result, an image mask method, as disclosed herein, may be usedto improve the dynamic range used for display of a captured image,thereby improving the quality of the display of the captured image.

[0087] One of the preferred implementations of the invention is as setsof computer readable instructions resident in the random access memoryof one or more processing systems configured generally as described inFIGS. 1-10. Until required by the processing system, the set ofinstructions may be stored in another computer readable memory, forexample, in a hard disk drive or in a removable memory such as anoptical disk for eventual use in a CD drive or DVD drive or a floppydisk for eventual use in a floppy disk drive. Further, the set ofinstructions can be stored in the memory of another image processingsystem and transmitted over a local area network or a wide area network,such as the Internet, where the transmitted signal could be a signalpropagated through a medium such as an ISDN line, or the signal may bepropagated through an air medium and received by a local satellite to betransferred to the processing system. Such a signal may be a compositesignal comprising a carrier signal, and contained within the carriersignal is the desired information containing at least one computerprogram instruction implementing the invention, and may be downloaded assuch when desired by the user. One skilled in the art would appreciatethat the physical storage and/or transfer of the sets of instructionsphysically changes the medium upon which it is stored electrically,magnetically, or chemically so that the medium carries computer readableinformation. The preceding detailed description is, therefore, not to betaken in a limiting sense, and the scope of the present invention isdefined only by the appended claims.

[0088] In the preceding detailed description of the figures, referencehas been made to the accompanying drawings which form a part thereof,and in which is shown by way of illustration specific preferredembodiments in which the invention may be practiced. These embodimentsare described in sufficient detail to enable those skilled in the art topractice the invention, and it is to be understood that otherembodiments may be utilized and that logical, mechanical, chemical andelectrical changes may be made without departing from the spirit orscope of the invention. To avoid detail not necessary to enable thoseskilled in the art to practice the invention, the description may omitcertain information known to those skilled in the art. Furthermore, manyother varied embodiments that incorporate the teachings of the inventionmay be easily constructed by those skilled in the art. Accordingly, thepresent invention is not intended to be limited to the specific form setforth herein, but on the contrary, it is intended to cover suchalternatives, modifications, and equivalents, as can be reasonablyincluded within the spirit and scope of the invention. The precedingdetailed description is, therefore, not to be taken in a limiting sense,and the scope of the present invention is defined only by the appendedclaims.

What is claimed is:
 1. A method for enhancing a digital imagecomprising: providing a digital original image comprised of a pluralityof pixels, wherein each pixel includes an original value correspondingto a characteristic of the image; calculating a dynamic image mask valuefor each pixel by averaging the original value of a pixel with theoriginal values of the pixels proximate that pixel having originalvalues lower than a threshold sharpness; and applying the dynamic imagemask value to the original value for each corresponding pixel using amathematical function to produce an enhanced image.
 2. The method ofclaim 1, wherein providing a digital original image comprises capturinga digital original image using a digital capture device.
 3. The methodof claim 1, wherein providing a digital original image comprisescapturing a digital original image using an imaging system.
 4. Themethod of claim 1, wherein the original value corresponding to acharacteristic of the image comprises an intensity value correspondingto a color.
 5. The method of claim 1, wherein the original valuecorresponding to a characteristic of the image comprises an intensityvalue corresponding to range of frequencies.
 6. The method of claim 1,wherein averaging the original value of a pixel with only the originalvalues of the pixels proximate that pixel having original values lessthan a sharpness threshold comprises averaging the original value of apixel with only the weighted original values of the pixels proximatethat pixel having original values less than a sharpness threshold. 7.The method of claim 6, wherein the weighted original values aredetermined according to the following formula:$w_{N} = ( {{1 - ( {\frac{{{pixel}N} - {centerpixel}}{Gain}} )},} $

wherein pixelN is the value of the pixel being weighed, center pixel isthe value of a central pixel, and wherein Gain is the thresholdsharpness.
 8. The method of claim 1, wherein the original values used tocalculate the difference less than the sharpness threshold correspond todifferent characteristics than the original values used in averaging. 9.The method of claim 1, wherein calculating a dynamic image mask valueincludes performing a pyramidal decomposition on the original image. 10.The method of claim 1, wherein the mathematical function comprisesdivision.
 11. The method of claim 1, wherein the mathematical functioncomprises: ${{OUT} = \frac{IN}{{\frac{3}{4}{MASK}} + \frac{1}{4}}},$

wherein OUT is the value of the pixel being calculated in the enhancedscanned image, IN is the value of the relative pixel in the originalimage, and MASK is the value of the relative pixel in the dynamic imagemask.
 12. The method of claim 1, further comprising performing histogramleveling to the enhanced scanned image.
 13. The method of claim 1,wherein the enhanced scanned image includes an image contrast and agrayscale contrast.
 14. The method of claim 13, wherein the imagecontrast and the grayscale contrast can be controlled independently ofeach other.
 15. The method of claim 1, wherein the dynamic image maskvalue may be proportionally varied by a user.
 16. A system comprising: asensor system operable to produce electronic signals corresponding tocertain characteristics of a subject; a processor operable to receivethe electronic signals and produce image values for each pixel; and amemory media having software stored thereon, wherein the software isoperable to: calculate a dynamic image mask value for each pixel byaveraging the image value of a pixel with the image values of the pixelsproximate that pixel having image values lower than a thresholdsharpness; and apply the dynamic image mask value to the image value foreach corresponding pixel using a mathematical function to produce anenhanced image.
 17. The system of claim 16, wherein the sensor systemoperates to measure light from the subject.
 18. The system of claim 16,wherein the sensor system operates to measure a magnetic resonancepulse.
 19. The system of claim 16, further comprising a printer operableto print the enhanced image.
 20. The system of claim 19, wherein theprinter comprises a photographic printer.
 21. The system of claim 16,further comprising a digital output device operable to store theenhanced image.
 22. The system of claim 16, wherein the system comprisesa digital device within the group of a digital camera and a videocamera.
 23. The system of claim 16, wherein the system comprises animaging system within the group of a magnetic resonance imaging systemand a radar system.
 24. The system of claim 16, wherein the software isloaded into an image capturing device.
 25. The system of claim 16,wherein the system comprises a printer device.
 26. A software tangiblyembodied in a computer readable medium, said software operable toproduce an enhanced image by implementing a method comprising:generating a dynamic image mask from a digital original image, thedynamic image mask and the original image each comprising a plurality ofpixels having varying values, wherein the values of the plurality ofdynamic image mask pixels are set to form sharper edges corresponding toareas of more rapidly changing pixel values in the original image andless sharp regions corresponding to areas of less rapidly changing pixelvalues in the original image; and combining the dynamic image mask withthe original image to produce the enhanced image.
 27. The software ofclaim 26, wherein: the original image includes an amount of image detailencoded in a physically reproducible dynamic range; and wherein theenhanced image includes an increased amount of detail encoded in thephysically reproducible dynamic range.
 28. The software of claim 26,wherein combining the dynamic image mask with the original image isperformed through mathematical manipulation.
 29. The software of claim28, wherein the mathematical manipulation includes division.
 30. Thesoftware of claim 26, wherein the pixels in the dynamic image mask aregenerated according to the equation,${{OUT} = \frac{IN}{{\frac{3}{4}{MASK}} + \frac{1}{4}}},$

wherein OUT is the value of the pixel being calculated in the enhancedimage, IN is the value of the relative pixel in the original image, andMASK is the value of the relative pixel in the dynamic image mask. 31.The software of claim 26, further comprising histogram leveling.
 32. Thesoftware of claim 26, wherein the value of a pixel in the dynamic imagemask is generated by averaging the value of a central pixelcorresponding to the pixel in the original image with weighted values ofa plurality of neighboring pixels in the original image.
 33. Thesoftware of claim 32, wherein the weighting of the plurality ofneighboring pixels is dependant on a proximity of the neighboring pixelsto the central pixel and a contrast of the plurality of neighboringpixels to the central pixel.
 34. The software of claim 26, wherein theweight of pixels in the dynamic image mask is determined according tothe following formula:$w_{N} = ( {{1 - ( {\frac{{{pixel}N} - {centerpixel}}{Gain}} )},} $

wherein pixelN is the value of the pixel being weighed, center pixel isthe value of the central pixel, and wherein Gain is a threshold contrastvalue for determining a sharp edge.
 35. The software of claim 26,wherein the value of a pixel in the dynamic image mask is generatedbased on a relationship of the value of a different characteristic. 36.The software of claim 26, wherein the generating the dynamic image maskincludes performing a pyramidal decomposition on the original image. 37.The software of claim 26, wherein the software is resident on acomputer.
 38. The software of claim 26, wherein the software is residenton a digital camera.
 39. A system comprising: an image sensor to convertlight reflected from an image into information representative of theimage; a processor; memory operably coupled to said processor; and aprogram of instructions capable of being stored in said memory andexecuted by said processor, said program of instructions to manipulatesaid processor to: obtain a dynamic image mask, the dynamic image maskand the information representative of the image each including aplurality of pixels having varying values, wherein the values of theplurality of dynamic image mask pixels are set to form sharper edgescorresponding to areas of more rapidly changing pixel values in theoriginal image and less sharp regions corresponding to areas of lessrapidly changing pixel values in the original image; and combine theimage mask with the information representative of the image to obtain amasked image.
 40. The system of claim 39, further including a colordecoder, operably connected to said image sensor, to generate colorinformation from the information representative of the image. 41 Thesystem of claim 40, wherein said program of instructions are executed onan output of said image sensor, and where a result of said executedprogram of instructions are input to said color decoder.
 42. The systemof claim 39, further including a color management system, operablyconnected to said color decoder, to process said color information. 43.The system of claim 42, wherein said program of instructions areexecuted on an output of said color decoder, and where a result of saidexecuted program of instructions are input to said color managementsystem.
 44. The system of claim 43, wherein said output of said colordecoder is information representative of a red portion of the image, agreen portion of the image, and a blue portion of the image.
 45. Thesystem of claim 42, further including a storage system, operablyconnected to said color management system, to store the colorinformation.
 46. The system of claim 45, wherein said program ofinstructions are executed on an output of said color management system,and where a result of said executed program of instructions are input tosaid storage system.
 47. The system of claim 39, further including adisplay, operable to display a representation of the informationrepresentative of the image.
 48. The system of claim 47, wherein saidprogram of instructions are executed on an output of a color managementsystem, and where a result of said executed program of instructions areinput to said display.